Cybercartography: Maps and Mapping in the Information Era
Bibliographic record
Abstract
The world of maps and mapping is rapidly being transformed. Recent technological developments have brought maps into the daily life of societies all over the world in unprecedented ways. Maps are everywhere: on our cell phones, in newspapers, in art galleries, on television, in books, and, obviously, on our computer screens. According to Michael Peterson (2005), maps are now second only to weather information in the number of World Wide Web search requests. This widespread use of on-line mapping has attracted the interest of large corporations such as Google, Yahoo, and Microsoft. Recently, the almost instantaneous success of Google Map, Google Earth, and Microsoft Digital Earth (Goodchild 2005) has demonstrated the increasing presence of maps in our daily life. This success is also transforming the way we access, use, and interact with maps. User-friendly technologies and high-resolution images now allow users to create maps that respond to individualized demands.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.015 | 0.033 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".